Abstract

Breast tumors consist of several different tissue components. Despite the heterogeneity, most gene expression analyses have traditionally been performed without prior microdissection of the tissue sample. Thus, the gene expression profiles obtained reflect the mRNA contribution from the various tissue components. We utilized histopathological estimations of area fractions of tumor and stromal tissue components in 198 fresh-frozen breast tumor tissue samples for a cell type-associated gene expression analysis associated with distant metastasis. Sets of differentially expressed gene-probes were identified in tumors from patients who developed distant metastasis compared with those who did not, by weighing the contribution from each tumor with the relative content of stromal and tumor epithelial cells in their individual tumor specimen. The analyses were performed under various assumptions of mRNA transcription level from tumor epithelial cells compared with stromal cells. A set of 30 differentially expressed gene-probes was ascribed solely to carcinoma cells. Furthermore, two sets of 38 and five differentially expressed gene-probes were mostly associated to tumor epithelial and stromal cells, respectively. Finally, a set of 26 differentially expressed gene-probes was identified independently of cell type focus. The differentially expressed genes were validated in independent gene expression data from a set of laser capture microdissected invasive ductal carcinomas. We present a method for identifying and ascribing differentially expressed genes to tumor epithelial and/or stromal cells, by utilizing pathologic information and weighted t-statistics. Although a transcriptional contribution from the stromal cell fraction is detectable in microarray experiments performed on bulk tumor, the gene expression differences between the distant metastasis and no distant metastasis group were mostly ascribed to the tumor epithelial cells of the primary breast tumors. However, the gene PIP5K2A was found significantly elevated in stroma cells in distant metastasis group, compared to stroma in no distant metastasis group. These findings were confirmed in gene expression data from the representative compartments from microdissected breast tissue. The method described was also found to be robust to different histopathological procedures.

Highlights

  • IntroductionFemale breast cancer counts for over 465 000 deaths annually world wide (approximately 1.300.000 new cases), and is the most common cancer type among women [1]

  • Female breast cancer counts for over 465 000 deaths annually world wide, and is the most common cancer type among women [1]

  • We provide here an in silico method to ascribe differentially expressed gene-probes (DEGs) in primary breast tumors between patients experiencing distant metastasis (DM) and patients experiencing no distant metastasis (NoDM) to either tumor epithelial and/or stroma cells

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Summary

Introduction

Female breast cancer counts for over 465 000 deaths annually world wide (approximately 1.300.000 new cases), and is the most common cancer type among women [1]. Earlier diagnosis and more efficient treatment strategies have reduced the mortality and lowered the risk for recurrence. Established molecular markers such as the estrogen receptor (ER), progesterone receptor (PgR) and human epidermal growth factor receptor 2 (HER2/neu) are considered routinely for treatment decisions [2]. Whole-genome DNA microarrays for gene expression have made it possible to unravel biological mechanisms underlying the disease at the transcriptomic level [3]. Gene expression profiling has shown that breast tumors can be classified into subgroups displaying distinct characteristics with respect to both clinical markers and patient outcome [4,5,6,7], which emphasizes that breast cancer is a heterogeneous disease that should be treated . Specific gene signatures have been identified that correlate with different aspects of the disease, including two risk-predictors for distant recurrence that are currently being tested in large clinical trials [8,9,10,11,12]

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